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Application of the Empirical Mode Decomposition method for the prediction of the tool wear in turning operation

机译：经验模态分解法在车削刀具磨损预测中的应用

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Tool wear is one of the factors to consider since its evolution damages and degrades the surface roughness of machined material. For this raison, this article proposes the application of a new time-frequency method, called Empirical Mode Decomposition, for the prediction of the tool wear in turning operation.The proposed method is applied on vibratory acceleration signals measured during the cutting process in different configurations. The sifting process of the EMD method decomposes the measured signal into several Intrinsic Modes Functions allowing its time-frequency analysis. A time and frequency domain analyses have been carried out.In time domain, the tool wear monitoring is performed by using two scalar indicators; the energy and the mean power of the first IMF of the EMD decomposition. The variation of these indicators over the entire tool life highlights the three periods and allows locating the transition point between the wear stabilization and the wear acceleration period. The prediction of the tool ageing is then very clear avoiding its collapse and the machining fail.In the frequency domain a new indicator, based on the amplitude of the peak corresponding to the tool’s natural frequency, is proposed. It has been shown that the variation of this indicator over the entire tool life is the same as for the scalar indicators. The three periods are shown and the critical transition point reflecting the beginning of the tool ageing is clearly detectable.The experimental results are very promising in industrial environment for the implementation of online monitoring system for cutting tool state.
机译：刀具磨损是要考虑的因素之一，因为其演变会损坏并降低加工材料的表面粗糙度。为此，本文提出了一种新的时频方法-经验模态分解（Empirical Mode Decomposition）的应用，以预测车削操作中的刀具磨损。该方法适用于不同配置下切削过程中测得的振动加速度信号。 EMD方法的筛选过程将测得的信号分解为几个固有模式函数，从而可以进行时频分析。进行了时域和频域分析。在时域中，通过使用两个标量指示器进行刀具磨损监测。 EMD分解的第一个IMF的能量和平均功率。这些指标在整个刀具寿命中的变化突出显示了这三个周期，并允许在磨损稳定和磨损加速周期之间找到过渡点。这样就可以非常清楚地预测刀具的老化，避免其塌陷和加工失败。在频域中，基于与刀具固有频率相对应的峰值幅度，提出了一种新的指标。已经表明，该指标在整个刀具寿命中的变化与标量指标相同。显示了这三个时期，并且可以清楚地检测到反映刀具老化开始的临界过渡点。实验结果在工业环境中对于实现切削刀具状态在线监控系统的实现非常有希望。

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